Protective effect of a nanofilled resin-based coating on wear resistance of glass ionomer cement restorative materials
Bibliographic record
Abstract
BACKGROUND: The effect of nanofilled resin-based coating on the wear resistance of glass ionomer cements (GICs) is still controversial. This study aims to compare the wear resistance of four encapsulated GICs including two conventional and two resin-modified, and to evaluate the effect of G-Coat Plus on the wear resistance of GICs. METHODS: A total of 80 disk-shaped specimens were prepared from two CGICs (riva self cure (SDI) and Equia Forte Fil (GC) and two RM- GICs (Ketac Nano (3 M/ESPE) and Fuji II LC (GC). The specimens of each material were divided into two groups (n = 10) based on the surface protection: no coating (NC), and coating with G-Coat Plus (GCP). All specimens were then placed in distilled water for 24 h at 37 °C. The specimens were subjected to thermocycling for 120,000 cycles using a chewing simulator. Wear resistance was measured using a specific formula. Data was analyzed using Kruskal-Wallis test. RESULTS: ) between coated and uncoated groups for all materials (P > 0.05). Ketac Nano showed significantly lower volume loss (0.65 ± 0.12) compared to all other groups (P < 0.05) among uncoated specimen, and significantly lower than Fuji II LC (P = 0.035) and Equia Forte Fil (P = 0.040) among coated groups. However, no statically significant difference was observed between volume loss of coated Ketac Nano with that of riva self cure (P = 0.087). CONCLUSIONS: Coating with GCP did not affect the wear depth of GICs, and Ketac Nano showed significantly lower volume loss regardless of coating.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".